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Priatna , Wowon
Informatika, Fakultas Ilmu Komputer, Universitas Bhayangkara Jakarta Raya
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Journal : IICS

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Sentiment Analysis of Bjorka Hacker Using the Naive Bayes and C.45 Algorithms Wowon Priatna; Eka Nur A’ini; Joni Warta; Agus Hidayat; Tyastuti Sri Lestari; Rasim
IAIC International Conference Series Vol. 4 No. 1 (2023): SEMNASTIK 2023
Publisher : IAIC Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/conferenceseries.v4i1.614

Abstract

 In 2023, Indonesia was again devastated by a hacker known as Bjorka. Bjorka did not act just once or twice; every time, Bjorka made the entire Indonesian population proud. The 19 million BPJS Employment data belonging to the Indonesian people that Bjorka hacked is the BPJS Employment data belonging to the Indonesian people that Bjorka hacked. Since the release of the Bjorka story, there has been a surge in the number of people criticizing it on social media, particularly Facebook, so the criticism or opinions can be used to conduct sentiment analysis. Based on this, developing a method that can automatically classify beliefs into positive and negative categories through sentiment analysis is necessary. The sentiment analysis process begins with data preprocessing, followed by keyword analysis using the TF-IDF method, algorithm development, and analysis of classification results. The data classification methods used in this study are Naive Bayes and C4.5. The data will be analyzed using text mining and classified using the Naive Bayes and C4.5 algorithms. Based on the results of the tests, the best classification was achieved by Nave Bayes, with a score of 70 percent for the C4.5 algorithm and 68 percent for the C4.5 algorithm. The Nave Bayes algorithm can predict up to 70% data transmission rates for both positive and negative signals.
Co-Authors -, Rasim Ade Iriani Agung Nugroho Agung Nugroho Agus Hidayat Aida Fitriyani, Aida Ajif Yunizar Pratama Yusuf Alexander, Allan D Alhillah, Yumaris Alfi Andi Lawrence Hutahaean, Johanes Andri Fajriya Andry Fadjriya Annisa Oktavianti Hermadi Asep R. Hamdani Asep Ramdhani M Asep Ramdhani Mahbub Atika , Prima Dina Dhea Putri Aprilyana Dwi Budi Srisulistiowati Dwipa Handayani Eka Nur A’ini Endang Retnoningsih Enggar Putera, dkk, Diaz Faisal Adi Saputra Fajar Mukharom Fathurrazi, Ahmad Febry Sandrian Sagala Fefbiansyah Hasibuan Galih Apriansha Pradana Hadi Kusmara Hendarman Lubis Herlawati Herlawati Hindriyanto Dwi Purnomo Ikhsan Romli Ilham Rizky Widianto Intan Safira Irwan Sembiring Ismaniah Ismaniah Joni Warta Joni Warta Joniwarta Joniwarta, Joniwarta Jumi Saroh Hidayat Kapriadi, Engkap Karyaningsih, Dentik Khoirunnisaa, Nabiilah Kustanto , Prio Lubis, Hendarman M. Fadhli Nursal Mayadi Mayadi Meutia, Kardinah Indrianna Mugiarso Mugiarso, Mugiarso Muhammad Khaerudin Noe’man,, Achmad Nurjeli Nurjeli Pradana , Galih Apriansha Prima Dina Atika Purnomo , Rakhmat Purnomo, Rakhmat Putra , Tri Dharma Rahmadya Trias Handayanto Rakhmat Purnomo Rasim Ratna Salkiawati Rejeki , Sri Retnoningsih , Endang Rinaldi Tunnisia Ritzkal, Ritzkal Rofiah , Syahbaniar Sagala, Febry Sandrian Saputra , Faisal Adi Silvi - Siti Setiawati Siti Setiawati Siti Setiawati Siti Setiawati, Andika Yusuf Hidayat Sri Lestari, Tyastuti Sri Rejeki Sudiantini, Dian Sulistiyo, Dwi Suryadi Syahbaniar Rofiah Tb Ai Munandar, Tb Ai Theopillus J. H. Wellem Tri Dharma Putra Tri Dharma Putra Tyastuti Sri Lestari Tyastuti Sri Lestari Tyastuti Sri Lestari Tyastuti Sri Lestari Widianto, Ilham Rizky Wiyanto Wiyanto